Consumers are suffering from electricity bills they find excessive, utility companies are suffering from overloaded grids, and the transition toward renewable energy underway in many advanced economies will only make these problems worse since most renewables cannot be scheduled; e. g., wind power is quite cheap when the wind is blowing, but unavailable when the wind is not blowing, and solar power is only available when the sun is shining. Brownouts are a specter that haunts the most advanced countries, and especially high-value-added industries that depend on electricity to process information.
Over the past decade or so, a development emerged that promises to alleviate these problems: the smart grid, and in particular real-time energy pricing. Under real-time energy pricing, electricity customers no longer pay a fixed price per kilowatt hour of energy they consume; rather, this price is allowed to float and to be determined by supply and demand. This brings many advantages: It stabilizes the aging power grid because peak loads get reduced as energy is more expensive at peak time and less expensive off-peak. It is good for consumers who have some flexibility in their energy use because it allows them to shift consumption from times when electricity is expensive to times when it is cheap. Real-time pricing also encourages the adoption of renewable, but unpredictable energy sources such as solar and wind power by providing a mechanism to steer electricity consumption toward times when supply from renewable sources is plentiful. Even with fossil fuels, real-time pricing can help the environment; during peak demand, old, inefficient, and dirty power plants normally held in reserve have to come online to meet the peak demand. By providing an incentive to consume less energy during peak demand times, real-time pricing can reduce the use of these most inefficient plants.
In some places, real-time electricity pricing is now available even for residential customers Illinois pioneered this development with legislation passed in 2006 that mandates the availability of real-time pricing for residential electricity customers. The problem is that there are not many systems available that allow electricity consumers, and in particular residential and commercial, as opposed to industrial, consumers to adapt their electricity use patterns to real-time prices. The most commonly used solutions are fairly crude: Some electricity providers offer customers an option whereby they can remotely switch off the customer's air conditioning system if the energy price exceeds a certain amount, or they offer to send customers emails or text messages to inform them of high prices so that customers can switch electric loads off.
The most important application where an automatic response to power prices would be desirable is in HVAC systems. In many residential and commercial buildings, air conditioning systems are the single biggest power consumers, and electricity prices typically peak when the weather gets unexpectedly warm and thus more air conditioning units than energy suppliers had expected come online Therefore, much of our discussion will be about smart thermostats than can regulate air conditioning units in response to changes in electricity prices.
Clearly, it would be desirable to have solutions that can react automatically to changes in energy prices and reduce energy consumption during periods of high prices and allow more energy consumption during periods of low prices. There are numerous systems that attempt to do just that in the patent literature, the most pertinent of which is cited above. With a few exceptions that we will discuss in more detail, they fall into two partially overlapping groups.
Common Approaches
The first group are rather simple contraptions that monitor the prevailing electricity price and switch off electrical consumers in response to increased prices. For example, thermostats may increase the cooling setpoint temperature or even entirely switch off air conditioning if energy prices exceed a certain level.
The second group of solutions in the patent literature are complex, centralized systems where a central system at the utility maintaining the grid communicates with systems at the customer site to switch electrical loads on and off. In the simplest case, this is a one-way communication whereby the utility company can remotely switch off electrical loads at the customer site when demand is high; this can either be a response to real-time pricing, or it can be implemented even in a system without real-time pricing where, for example, the customer simply obtains a fixed rebate for giving the power company the privilege to switch off his air conditioner at times of high demand.
These systems have a number of serious disadvantages that limit their efficiency, effectiveness, adoption, and sophistication, however. They need a separate one-way communications channel from the power company to the customer to communicate commands to potentially many millions of electric loads. If this is done by a central command to all loads at once, the power grid may not exactly get stabilized from all of these loads going on- and offline at exactly the same time; if each device is addressed separately, there is a lot of data traffic to carry, and it is not easy to decide on an equitable system to determine which customer gets shut off when. These systems also don't offer a lot of flexibility since the communication is one-way and thus the customer is not able to communicate special situations: for example, someone may normally be willing to have his air conditioning shut off during peak times, but may very much want the air conditioning on a few occasions when his child is sick or he is hosting an important visitor. If the utility company had more information about each customer's wants and needs, the task of finding the best solution to meet these wants and needs would still be truly daunting. Relying on centralized decisionmaking also introduces a central point of failure. Finally, relying on a centralized structure creates regulatory and psychological hurdles to adoption of new, innovative ideas: The electricity grid is heavily regulated, and adoption of innovations will often require the approval of legislators or regulators, which at the very least tends to be time-consuming. Consumers may also simply not like the idea very much of an operator at some remote control desk switching their air conditioning on and off at his, not their, pleasure.
The more advanced systems in the second group of solutions in the existing literature are more ambitious and use a two-way communication channel between the utility company and the customer site. For example, U.S. Pat. No. 5,926,776 teaches a smart thermostat that receives the current energy price from the utility company. The user can set different temperature setpoints for different energy price brackets, such as low, medium, and high prices. The thermostat then sets these temperature setpoints and and transmits the current temperature and temperature setpoint back to the utility company. This requires a very tight integration between the thermostat and the utility company, which the patent, perhaps a little comically, proposes to put to extra use by also using the thermostat as a terminal to pay the monthly energy bill.
These more advanced centralized systems ameliorate the problem of the decisionmaker, i. e., the utility company, not having information about each customer's wants and needs, but they exacerbate most of the other problems we discussed. Finding the optimal combination of which of millions of electrical devices to switch on and off is a problem that is extremely hard to solve on a centralized server: switching millions of loads on and off is a huge integer optimization problem for which a global optimum is very hard to find. If the communication channel between the utility company and the customer site breaks down, the customer might be stuck without properly functioning heating and air conditioning, even though the power grid itself works perfectly well. The adoption of new ideas is difficult since everything at the customer site is tightly coupled to a huge system with lots of organizational inertia. Consumers may also not be particularly willing to transmit information about each electrical device they are using to a power company.
Advanced Approaches
After having discussed the two most common approaches, we now turn to four more interesting inventions in the patent literature that go beyond the approaches we discussed so far.
The approaches we have seen so far only considered the current energy price and then, in the case of controlling an HVAC system, adjust the setpoints of a thermostat accordingly. U.S. Pat. No. 8,359,124 teaches an energy optimization system that goes beyond this approach and can look into the future. It uses an optimization module that can run either at the customer site or at a centralized server run by the utility company. This makes it possible to adjust thermostat setpoints or other electrical appliance loads not only based upon the current energy price, but also to take into account the tradeoff between using energy now at the current price or using energy later at the price that will prevail then.
In this context, it is interesting to observe that virtually all of the prior art appears to assume that electricity prices, either for the current time period or for future time periods, are and can be known at the time an optimization process is run. As we shall see later, this assumption does not actually hold in most electricity markets.
Like much of prior art, this patent assumes a fairly centralized system with constant data exchange between the utility company and the customer. Specifically, this patent teaches a tight integration between computer systems owned and managed by the utility and computer systems with a central server “configured to exchange customer site information comprising measured information, predicted information and customer input information with each of the customer sites.” This approach brings all the disadvantages of centralized systems we have discussed above.
U.S. Pat. No. 8,359,124 also discusses controlling an HVAC system as part of its optimization system. It is worth noting that the optimizer in this patent “uses the the thermal model to calculate the optimum temperature setpoint for all zones in the customer site so as to minimize the cost of heating or cooling required for maintaining the temperatures within a user defined comfort zone.” This is to say, the optimizer does not control the operation state of an air conditioning compressor directly but rather does so indirectly by setting wider or narrower temperature limits on a conventional thermostat. The inventors might have chosen this approach either for easy integration with existing heating systems or in order to solve the problem, as their patent teaches, as “a standard optimization problem (e.g., linear programming, quadratic programming, nonlinear programming, etc.)” which requires the variables to be optimized to be continuous. Thermostat setpoints are continuous variables, whereas an air conditioning compressor typically can only be in discrete states such as off or on or perhaps off and one of two different power levels, but not be run at, for example, 30% of power. Solving optimization problems where the variables to be optimized are discrete is known in the art to be a much harder problem than solving problems where the variables are continuous; specifically, solving optimization problems for discrete variables is NP-hard. Although the approach of setting temperature setpoints instead of controlling an air conditioning compressor or a furnace directly simplifies the math and improves backward compatibility, it also reduces cost efficiency because the optimizer cannot control the load directly to place usage in time intervals with the lowest energy costs. In particular, controlling temperature setpoints instead of controlling the discrete states of the air conditioning compressor or other equipment makes it hard or impossible to exploit short-term fluctuations in energy prices. (Of course, one can control an air conditioning compressor or furnace by setting a thermostat's temperature setpoints to very low or high values that are guaranteed to force the device on or off, but at this point the problem degenerates into an integer programming problem and techniques like linear or quadratic programming will not arrive at a satisfactory solution.)
US Patent Application 2013,008,5616 follows a very similar approach to U.S. Pat. No. 8,359,124. The primary difference is that Application 2013,008,5616 instead of using a thermal model of the building in question uses a training day to learn the thermal characteristics of the building. Like U.S. Pat. No. 8,359,124, Application 2013,008,5616 does not propose direct control of the discrete states of the air conditioning equipment, but instead optimizes a “setpoint trajectory” for one or more thermostats' temperature setpoints using a linear operator.
US Patent Application 2012,018,5106 goes beyond optimizing continuous variables such as temperature setpoints and formulates a problem of power-grid optimization explicitly as an integer programming problem, which “then can be solved using Optimization Subroutine Library OSL, CPLEX®.” Unfortunately, the Application does not quite explain how exactly these libraries are to be used, but presumably the idea is to use their mixed-integer programming subroutines which employ branch-and-bound or supernode processing and impose serious restrictions on the permissible models and objective functions. At any rate, the Application proposes this approach for optimizing power generation, not power consumption, and since power plants tend to be much larger facilities and fewer in number than power consumers, resources to make these optimization strategies work may be affordable for companies running power plants, but certainly are not so for consumers wishing to regulate residential appliances such as air conditioners. Even ignoring the cost of the CPLEX library and the hardware to run it on, the solution proposed would appear rather hard to adapt to applications like a residential thermostat that typically uses a microcontroller with severely constrained memory and computational resources, and the Application does not contemplate such a use. The use of method like branch-and-bound or supernode processing implies serious restrictions of thermal and energy models that can be used which may make it difficult to adapt the method to real-world problems, and even with well-chosen models will often have trouble finding an optimal solution.
WIPO Patent Application WO 2013,019,990 proposes using the air conditioning in data centers as spare capacity to stabilize power grids. This Application does not appear to teach a specific method for calculating an optimal cooling strategy, but from FIGS. 3 and 4 of that Application it appears that the idea proposed is to change the temperature setpoint of a thermostat to a fixed lower value while energy prices are low, but prices a few hours out are expected to be high, and then to change the temperature setpoint back to a fixed normal value. The question left open is to find out what one would consider a ‘low’ or ‘high’ price.
US Patent Application 2013,019,0940 teaches methods involving the use of weather predictions to optimize energy cost. It does not however, disclose how, specifically, this is to be done. For example, the Application suggests the “the optimizing and scheduling module [ . . . ] adjusts the series of temperature set points to provide additional cooling (i.e., pre-cool) to the home in the earlier part of the morning (e.g., 8:30 am) so that the air conditioner in the home does not need to run as long at 11:00 am when the exterior temperature is hotter. Also, the optimizing and scheduling module 210 understands that the price of energy at 8:30 am is lower than the predicted cost at 11:00 am, so an increased consumption of energy in the early morning achieves a cost savings versus consuming more energy at the later time of 11:00 am.” It does not, however, give a method to decide when to precool, how much, or how to arrive at estimates of energy cost, but leaves that question at the rather laconic “[s]everal mathematical algorithms can be used in developing possible predictions of the energy consumed by buildings connected to the system 100, as well as predicting the specific amount of energy devoted to the operation of HVAC,” and the reader to wonder whether there are any algorithms particularly suitable to the job.
From this review of the prior art a certain malaise in controlling electric loads, and HVAC systems in particular, with respect to energy prices becomes apparent. The prior art has recognized this to be a problem in need of a solution for quite some time, but the existing proposals suffer from severe problems. Centralized solution methods either are very crude and control different loads with different business needs in the same way, such as by simply shutting them off, or become computationally infeasible. Attempts at more sophisticated control that takes the business needs of each location into account propose methods that will not work very well or not at all, or simply don't suggest any particular method how to solve the problem. All of the prior art that attempts to take future electricity prices into account appears to assume that these future electricity prices are already known when in fact under many modern electricity markets the final electricity price for a time period will only be revealed once the time period is over. Thus, there is a clear and long-felt need for devices and methods that overcome these problems.